短期课程
题 目:Compressed Sensing and High-Dimensional Linear Regression
报 告 人： T. Tony Cai 教授
The Wharton School, University of Pennsylvania, USA
时 间：4月25日（周五）
上午10：00-11：30下午2：30-4：00
地 点：数学楼第二报告厅
Abstract: This short course will focus on compressed sensing and high dimensional linear
regression. These and other related problems have attracted much recent interest in a range
of fields including statistics, machine learning and electrical engineering. In the high dimensional
setting where the dimension p can be much larger than the sample size n, classical methods and
results based on fixed p and large n are no longer applicable. We will analyze in detail the
constrained and penalized l1 minimization methods for compressed sensing/high-dimensional
regression and give a unified and elementary analysis on sparse signal recovery in three
settings: noiseless, bounded noise and Gaussian noise.
欢迎有兴趣的老师与同学参加！
统计研究院
2014年4月17日